Row 90452

Row ID: 90452 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 90452 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Yes, only the two positive passages will be ranked as a 1 and the others are lablled as a 0. If there are other documents (outside of the two labelled positive cases) that are relevant to your query, then you are out of luck, and you cannot accurately compute ndcg (you may retrieve documents thst are not in ur golden set, but are truly relevant). I guess we need to know the details on ur evaluation dataset.

You can also just score the 12 passages using ur retriever, and compute the ndcg using that way. But that's not a super accurate way to compute ndcg@k assuming you have a much larger corpus than the listed 12 passages.

FieldValue
text Yes, only the two positive passages will be ranked as a 1 and the others are lablled as a 0. If there are other documents (outside of the two labelled positive cases) that are relevant to your query, then you are out of luck, and you cannot accurately compute ndcg (you may retrieve documents thst are not in ur golden set, but are truly relevant). I guess we need to know the details on ur evaluation dataset. You can also just score the 12 passages using ur retriever, and compute the ndcg using …
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-25
username_encoded Z0FBQUFBQm5Lak1yNVloOVFjNHdWbU9nV2FZLWpFX3pJdDA0Y3Q2MVdBRTB0ZmZyZEFRaEE5MW5aZ2t5WWN2eVZ0UWllaTQ5MnJYS243cUUwZHJEYTBramlvaElUXzh2YVE9PQ==
url_encoded Z0FBQUFBQm5Lak85d1pFU0JnWUdmb0NFRlpMM2VocjZzTmgwZXNLWTdfMVNiMy1IOFNPc2p3bUpBSVZ3MXNKTVZLcHZqZ0dnbUZZUXVOa2I5UGthTG0wUDNUd3p2QXIzOXZXV0xpRkdEdC1Xb3Y5TnRRb1ZjMUd5bkd3WHVjX3oxWFduaTQ2TDBWQlZmb0R3UWt0ZXIzTVYwcFJfaWF4aDNvSWFaMDh3cGh0TWkzaEQ0dHdhSDYwSU8wWUtrYks2SmhTMEViYV8zb2xybUJuUElCbTFlY3dPWWVMOVEyVDF5UT09

Raw Record

{
  "text": "Yes, only the two positive passages will be ranked as a 1 and the others are lablled as a 0. If there are other documents (outside of the two labelled positive cases) that are relevant to your query, then you are out of luck, and you cannot accurately compute ndcg (you may retrieve documents thst are not in ur golden set, but are truly relevant). I guess we need to know the details on ur evaluation dataset. \n\nYou can also just score the 12 passages using ur retriever, and compute the ndcg using that way. But that's not a super accurate way to compute ndcg@k assuming you have a much larger corpus than the listed 12 passages.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-25",
  "username_encoded": "Z0FBQUFBQm5Lak1yNVloOVFjNHdWbU9nV2FZLWpFX3pJdDA0Y3Q2MVdBRTB0ZmZyZEFRaEE5MW5aZ2t5WWN2eVZ0UWllaTQ5MnJYS243cUUwZHJEYTBramlvaElUXzh2YVE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak85d1pFU0JnWUdmb0NFRlpMM2VocjZzTmgwZXNLWTdfMVNiMy1IOFNPc2p3bUpBSVZ3MXNKTVZLcHZqZ0dnbUZZUXVOa2I5UGthTG0wUDNUd3p2QXIzOXZXV0xpRkdEdC1Xb3Y5TnRRb1ZjMUd5bkd3WHVjX3oxWFduaTQ2TDBWQlZmb0R3UWt0ZXIzTVYwcFJfaWF4aDNvSWFaMDh3cGh0TWkzaEQ0dHdhSDYwSU8wWUtrYks2SmhTMEViYV8zb2xybUJuUElCbTFlY3dPWWVMOVEyVDF5UT09"
}
Explore Dataset Explore Row

Entry Information